Investigation of Experimental and Computational Approaches to Optimize the Bacterial Three-Hybrid Assay

Abstract

Post-transcriptional gene regulation by non-coding small regulatory RNAs (sRNAs) plays an important role in bacterial stress responses and virulence. In many bacteria, the binding of sRNAs to their target mRNAs at or near the ribosome binding sites is often facilitated by protein chaperones such as Hfq or ProQ. In order to elucidate the molecular mechanisms of RNA-protein interactions, an in vivo genetic approach, the bacterial three-hybrid (B3H) assay, was developed to detect the binding of RNA with multiple RNA chaperones by connecting the strength of an RNA-protein interaction to the expression of a reporter gene. Despite the promise of the B3H system and its success in detecting many high-affinity interactions, low signal-to-noise for other RNA-protein interactions currently limits the broader utility of the assay. My study aims to optimize the hybrid RNA component of the assay to improve the breadth of detectable B3H interactions. To this end, I have focused on designing new hybrid RNA constructs with the addition of a GC-clamp - a short insert of guanines (G) and cytosines (C) flanking a region of interest - to promote proper folding and optimal display of RNA. My results demonstrate the potential of the short GC-clamp in improving the B3H detection of a broader range of sRNA-Hfq interactions. I have also explored the contributions of different components of the hybrid mRNA construct, including a stop codon, a short GC clamp and a trpA terminator, to mRNA expression level with the hope to understand how mRNA degradation might hinder the B3H detection of mRNA-Hfq interactions. To prioritize future experimental optimization, it is also imperative to understand this genetic toolbox from a systematic perspective. Using COPASI, a software package specializing in setting up and analyzing kinetic models, I have created a mathematical model of the B3H system and used the information from model simulations to predict the signal for RNA-protein interactions and guide further optimizing strategies. The work I present here represents significant progress towards increasing the sensitivity and generalizability of the B3H assay to study bacterial RNA-protein interactions which are implicated in many important processes in bacteria such as adaptation to stress, biofilm formation, virulence, and antibiotic resistance.